scholarly journals Partially Synthesised Dataset to Improve Prediction Accuracy

2016 ◽  
pp. 855-866
Author(s):  
Ahmed J. Aljaaf ◽  
Dhiya Al-Jumeily ◽  
Abir J. Hussain ◽  
Paul Fergus ◽  
Mohammed Al-Jumaily ◽  
...  
Keyword(s):  
Prediction Accuracy

Misperception of randomness decreases prediction accuracy

2009 ◽  
Author(s):  
Benjamin Scheibehenne ◽  
Andreas Wilke ◽  
Peter M. Todd
Keyword(s):  
Prediction Accuracy

Glycemia Prediction Accuracy of Simple Linear Models with Online Parameter Identification

2016 ◽  
Vol 9 (5) ◽  
pp. 367 ◽  
Author(s):  
Matúš Rebro ◽  
Marián Tárník ◽  
Ján Murgaš
Keyword(s):  
Parameter Identification ◽  
Prediction Accuracy ◽  
Linear Models

Generic Metrics for the Estimation of the Prediction Accuracy of Aircraft to Aircraft Conflicts by a Strategic Conflict Probe Tool

1999 ◽  
Vol 7 (3) ◽  
pp. 147-165 ◽  
Author(s):  
Mike M. Paglione ◽  
Hollis F. Ryan ◽  
Mary Lee Cale
Keyword(s):  
Prediction Accuracy ◽  
Strategic Conflict

TESTING PREDICTION ACCURACY OF HDU ADMISSION FOLLOWING HIGH GRADE SEROUS ADVANCED OVARIAN CANCER CYTOREDUCTIVE SURGERY USING MACHINE LEARNING METHODS.

2020 ◽  
Author(s):  
Alexandros Laios ◽  
Angelika Kaufmann ◽  
Mohamed Otify ◽  
Diederick De Jong ◽  
Tim Broadhead ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Cytoreductive Surgery ◽  
Prediction Accuracy ◽  
Advanced Ovarian Cancer ◽  
High Grade ◽  
Learning Methods ◽  
Machine Learning Methods

The microcomputer scientific software series 4: testing prediction accuracy.

1986 ◽  
Author(s):  
H. Michael Rauscher
Keyword(s):  
Prediction Accuracy ◽  
Scientific Software

Using Near Infrared Reflectance Product Library Files to Improve Prediction Accuracy and Reduce Calibration Costs

Crop Science ◽  
1993 ◽  
Vol 33 (3) ◽  
pp. 578-581 ◽  
Author(s):  
John S. Shenk ◽  
Steven L. Fales ◽  
Mark O. Westerhaus
Keyword(s):  
Prediction Accuracy ◽  
Near Infrared ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance

The Convolution Neural Network Combined with the HT Person Fit Statistic to Develop an APP for Detecting Dengue Fever in Children: Development and Usability Study (Preprint)

2019 ◽  
Author(s):  
CHIEN WEI ◽  
Chi Chow Julie ◽  
Chou Willy
Keyword(s):  
Neural Network ◽  
Dengue Fever ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Family Members ◽  
Convolution Neural Network ◽  
Public Health Issue ◽  
Person Fit ◽  
Model Accuracy ◽  
Comparison Results

UNSTRUCTURED Backgrounds: Dengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN), a well-established deep learning method, can improve prediction accuracy on account of its usage of a large number of parameters for modeling. Whether the HT person fit statistic can be combined with CNN to increase the prediction accuracy of the model and develop an application (APP) to detect DF in children remains unknown. Objectives: The aim of this study is to build a model for the automatic detection and classification of DF with symptoms to help patients, family members, and clinicians identify the disease at an early stage. Methods: We extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF) using CNN to predict DF risk. The accuracy of two sets of characteristics (19 symptoms and four other variables, including person mean, standard deviation, and two HT-related statistics matched to DF+ and DF−) for predicting DF, were then compared. Data were separated into training and testing sets, and the former was used to predict the latter. We calculated the sensitivity (Sens), specificity (Spec), and area under the receiver operating characteristic curve (AUC) across studies for comparison. Results: We observed that (1) the 23-item model yields a higher accuracy rate (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90) based on the 177-case training set; (2) the Sens values are almost higher than the corresponding Spec values (90% in 10 scenarios) for predicting DF; (3) the Sens and Spec values of the 23-item model are consistently higher than those of the 19-item model. An APP was subsequently designed to detect DF in children. Conclusion: The 23-item model yielded higher accuracy rates (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90). An APP could be developed to help patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.

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